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Refined Wasserstein Distributionally Robust Optimization for Contract Pricing: The Value of Optimality Conditions in Transactions.

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Τίτλος: Refined Wasserstein Distributionally Robust Optimization for Contract Pricing: The Value of Optimality Conditions in Transactions.
Συγγραφείς: Yu, Guodong1 (AUTHOR) iseyg@sdu.edu.cn, Dong, Pengcheng1 (AUTHOR) iedpc@mail.sdu.edu.cn, Sun, Huiping1 (AUTHOR) mseshp@mail.sdu.edu.cn
Πηγή: INFORMS Journal on Computing. Mar/Apr2026, Vol. 38 Issue 2, p397-413. 17p.
Θεματικοί όροι: *Linear programming, *Information asymmetry, *Transaction costs, Robust optimization, Lagrange multiplier, Inverse problems, Convex programming, Optimization algorithms
Περίληψη: This paper introduces a refined Wasserstein distributionally robust optimization (RWDRO) model to address contract pricing under information asymmetry. Our RWDRO model improves on traditional Wasserstein DRO (WDRO) models that rely solely on pure demand data by refining the Wasserstein ambiguity set through inverse optimization techniques applied to the buyer's historical order data. To address the computational challenges arising from semi-infinite programming in determining the new center distribution of the Wasserstein ball with dual-source data, we propose an equivalent linear programming approach leveraging Lagrange duality and set partitioning techniques. Then, we establish bounds for the buyer's worst-case order quantity and the seller's worst-case profit using first-order conditions. For dependent multiproduct cases, we propose a partition-based cutting-plane algorithm to obtain an σ-optimal solution. For single-product and independent multiproduct cases, we develop a tractable second-order cone programming model. Numerical experiments highlight the superior out-of-sample performance of RWDRO over traditional WDRO models, especially in small-data regimes, and the computational efficiency of our proposed solution methods. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72271145, 72134004, and 92367202]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0547) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2024.0547). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]
Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Refined Wasserstein Distributionally Robust Optimization for Contract Pricing: The Value of Optimality Conditions in Transactions.
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  Data: <searchLink fieldCode="JN" term="%22INFORMS+Journal+on+Computing%22">INFORMS Journal on Computing</searchLink>. Mar/Apr2026, Vol. 38 Issue 2, p397-413. 17p.
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  Data: This paper introduces a refined Wasserstein distributionally robust optimization (RWDRO) model to address contract pricing under information asymmetry. Our RWDRO model improves on traditional Wasserstein DRO (WDRO) models that rely solely on pure demand data by refining the Wasserstein ambiguity set through inverse optimization techniques applied to the buyer's historical order data. To address the computational challenges arising from semi-infinite programming in determining the new center distribution of the Wasserstein ball with dual-source data, we propose an equivalent linear programming approach leveraging Lagrange duality and set partitioning techniques. Then, we establish bounds for the buyer's worst-case order quantity and the seller's worst-case profit using first-order conditions. For dependent multiproduct cases, we propose a partition-based cutting-plane algorithm to obtain an σ-optimal solution. For single-product and independent multiproduct cases, we develop a tractable second-order cone programming model. Numerical experiments highlight the superior out-of-sample performance of RWDRO over traditional WDRO models, especially in small-data regimes, and the computational efficiency of our proposed solution methods. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72271145, 72134004, and 92367202]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0547) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2024.0547). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1287/ijoc.2024.0547
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        Text: English
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        PageCount: 17
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    Subjects:
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        Type: general
      – SubjectFull: Information asymmetry
        Type: general
      – SubjectFull: Transaction costs
        Type: general
      – SubjectFull: Robust optimization
        Type: general
      – SubjectFull: Lagrange multiplier
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      – SubjectFull: Inverse problems
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      – SubjectFull: Convex programming
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
    Titles:
      – TitleFull: Refined Wasserstein Distributionally Robust Optimization for Contract Pricing: The Value of Optimality Conditions in Transactions.
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            NameFull: Yu, Guodong
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            NameFull: Dong, Pengcheng
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            NameFull: Sun, Huiping
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              M: 03
              Text: Mar/Apr2026
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              Y: 2026
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